Total Articles: 20 from 7 sources
AWS News
1. Introducing OpenClaw on Amazon Lightsail to run your autonomous private AI agents
Published: 2026-03-04 20:04
Summary:
AWS launches OpenClaw on Amazon Lightsail to run OpenClaw instance, pairing your browser, enabling AI capabilities, and optionally connecting messaging channels Your Lightsail OpenClaw instance is pre-configured with Amazon Bedrock for starting with your AI assistant immediately — no additional configuration required.
2. AWS Weekly Roundup: OpenAI partnership, AWS Elemental Inference, Strands Labs, and more (March 2, 2026)
Published: 2026-03-02 19:05
Summary:
This past week, I’ve been deep in the trenches helping customers transform their businesses through AI-DLC (AI-Driven Lifecycle) workshops Throughout 2026, I’ve had the privilege of facilitating these sessions for numerous customers, guiding them through a structured framework that helps organizations identify, prioritize, and implement AI use cases that deliver measurable business value
3. AWS Security Hub Extended offers full-stack enterprise security with curated partner solutions
Published: 2026-02-26 18:52
Summary:
AWS announces the general availability of AWS Security Hub Extended, a unified, full-stack enterprise security solution It brings together AWS detection services and curated partner solutions through a single, simplified experience.
Dropbox Engineering
1. Using LLMs to amplify human labeling and improve Dash search relevance
URL: https://dropbox.tech/machine-learning/llm-human-labeling-improving-search-relevance-dropbox-dash
Published: 2026-02-26 17:00
Summary:
How we train Dash’s search ranking models with a mix of human and LLM-assisted labeling.
2. How low-bit inference enables efficient AI
URL: https://dropbox.tech/machine-learning/how-low-bit-inference-enables-efficient-ai
Published: 2026-02-12 18:00
Summary:
Making products like Dropbox Dash accessible to individuals and businesses means tackling new challenges around efficiency and resource use.
3. Insights from our executive roundtable on AI and engineering productivity
Published: 2026-02-11 17:00
Summary:
From Claude Code to Cursor, we’re big adopters of AI coding tools at Dropbox The early results have been promising, but there are still a lot of open questions about how to work with these tools most effectively and where they can have the most impact To push this conversation forward, we hosted an executive roundtable at our San Francisco studio
GitHub Blog
1. 60 million Copilot code reviews and counting
URL: https://github.blog/ai-and-ml/github-copilot/60-million-copilot-code-reviews-and-counting/
Published: 2026-03-05 20:10
Summary:
How Copilot code review helps teams keep up with AI-accelerated code changes The post 60 million Copilot code reviews and counting appeared first on The GitHub Blog.
2. Scaling AI opportunity across the globe: Learnings from GitHub and Andela
Published: 2026-03-05 17:00
Summary:
Developers connected to Andela share how they’re learning AI tools inside real production workflows The post Scaling AI opportunity across the globe: Learnings from GitHub and Andela appeared first on The GitHub Blog.
3. How we rebuilt the search architecture for high availability in GitHub Enterprise Server
Published: 2026-03-03 18:45
Summary:
Here’s how we the search experience better, faster, and more resilient for GHES customers The post How we rebuilt the search architecture for high availability in GitHub Enterprise Server appeared first on The GitHub Blog.
Google Developers
1. Conductor: Introducing context-driven development for Gemini CLI
URL: https://developers.googleblog.com/conductor-introducing-context-driven-development-for-gemini-cli/
Published: 2026-03-06 09:35
Summary:
Conductor is a new Gemini CLI extension that promotes context-driven development It shifts project context from chat logs to persistent Markdown files for formal specs and plans, ensuring AI agents adhere to project goals, style, and tech stack This structured workflow is great for “brownfield” projects and teams, allowing for safe iteration and consistent code contributions while keeping the human developer in control.
2. Introducing Agent Development Kit for TypeScript: Build AI Agents with the Power of a Code-First Approach
Published: 2026-03-06 09:35
Summary:
Introducing the Agent Development Kit (ADK) for TypeScript, an open-source framework for building complex, multi-agent AI systems with a code-first approach Developers can define agent logic in TypeScript, applying traditional software development best practices (version control, testing) ADK offers end-to-end type safety, modularity, and deployment-agnostic functionality, leveraging the familiar TypeScript/JavaScript ecosystem.
3. Real-World Agent Examples with Gemini 3
URL: https://developers.googleblog.com/real-world-agent-examples-with-gemini-3/
Published: 2026-03-06 09:35
Summary:
Gemini 3 is powering the next generation of reliable, production-ready AI agents This post highlights 6 open-source framework collaborations (ADK, Agno, Browser Use, Eigent, Letta, mem0), demonstrating practical agentic workflows for tasks like deep search, multi-agent systems, browser and enterprise automation, and stateful agents with advanced memory Clone the examples and start building today.
Meta Engineering
1. FFmpeg at Meta: Media Processing at Scale
URL: https://engineering.fb.com/2026/03/02/video-engineering/ffmpeg-at-meta-media-processing-at-scale/
Published: 2026-03-02 20:00
Summary:
FFmpeg is truly a multi-tool for media processing For the people who use our apps, FFmpeg plays an important role in enabling new video experiences […] Read More The post FFmpeg at Meta: Media Processing at Scale appeared first on Engineering at Meta.
2. Investing in Infrastructure: Meta’s Renewed Commitment to jemalloc
Published: 2026-03-02 17:00
Summary:
Meta recognizes the long-term benefits of jemalloc, a high-performance memory allocator, in its software infrastructure We are renewing focus on jemalloc, aiming to reduce maintenance needs and modernize the codebase while continuing to evolve the allocator to adapt to the latest hardware and workloads We are committed to continuing to develop jemalloc development with the […] Read More
3. RCCLX: Innovating GPU Communications on AMD Platforms
Published: 2026-02-24 21:30
Summary:
We are open-sourcing the initial version of RCCLX – an enhanced version of RCCL that we developed and tested on Meta’s internal workloads RCCLX is fully integrated with Torchcomms and aims to empower researchers and developers to accelerate innovation, regardless of their chosen backend The post RCCLX: Innovating GPU Communications on AMD Platforms appeared first on Engineering at Meta.
Netflix TechBlog
1. Optimizing Recommendation Systems with JDK’s Vector API
Published: 2026-03-03 01:36
Summary:
When we looked at CPU profiles for this service, one feature kept standing out: video serendipity scoring — the logic that answers a simple question:“How different is this new title from what you’ve been watching so far?”This single feature was consuming about 7.5% of total CPU on each node running the service More crucially for us, it’s pure Java: no native dependencies, no JNI transitions, and a development model that looks like normal Java code rather than platform-specific assembly or intrinsics.This was a particularly good match for our workload because we had already moved embeddings into flat, contiguous double[] buffers, and the hot loop was dominated by large numbers of dot products So we designed the fallback behavior explicitly: At startup, we detect Vector API support and use the SIMD batched matmul when available; otherwise we fall back to an optimized scalar path, with single-video requests continuing to use the per-item implementation.That gives us a clean operational story: services can opt in to the Vector API for maximum performance, but the system remains safe and predictable without it.Results in Production:With the full design in place with batching, flat buffers, ThreadLocal reuse, and the Vector API, we ran canaries that run production traffic
2. Mount Mayhem at Netflix: Scaling Containers on Modern CPUs
Published: 2026-02-28 22:55
Summary:
Examining the mount table made it clear that these mounts were related to container creation.The affected nodes were almost all r5.metal instances, and were starting applications whose container image contained many layers (50+).ChallengeMount Lock ContentionThe flamegraph in Figure 1 clearly shows where containerd spent its time Almost all of the time is spent trying to grab a kernel-level lock as part of the various mount-related activities when assembling the container’s root filesystem!Figure 1: Flamegraph depicting lock contentionLooking closer, containerd executes the following calls for each layer if using user namespaces:open_tree() to get a reference to the layer / directorymount_setattr() to set the idmap to match the container’s user range, shifting the ownership so this container can access the filesmove_mount() to create a bind mount on the host with this new idmap appliedThese bind mounts are owned by the container’s user range and are then used as the lowerdirs to create the overlayfs-based rootfs for the container The kernel VFS has various global locks related to the mount table, and each of these mounts requires taking that lock as we can see in the top of the flamegraph
3. MediaFM: The Multimodal AI Foundation for Media Understanding at Netflix
Published: 2026-02-23 18:24
Summary:
Consisting of tens of millions of individual shots across multiple titles, our diverse yet entertainment-specific dataset provides the perfect foundation to train multimodal media understanding models that enable many capabilities across the company such as ads relevancy, clip popularity prediction, and clip tagging.For these reasons, we developed the Netflix Media Foundational Model (MediaFM), our new, in-house, multimodal content embedding model For each shot, we generate three distinct embeddings from its core modalities:Video: an internal model called SeqCLIP (a CLIP-style model fine-tuned on video retrieval datasets) is used to embed frames sampled at uniform intervals from segmented shotsAudio: the audio samples from the same shots are embedded using Meta FAIR’s wav2vec2Timed Text: OpenAI’s text-embedding-3-large model is used to encode the corresponding timed text (e.g., closed captions, audio descriptions, or subtitles) for each shotFor each shot, the three embeddings² are concatenated and unit-normed to form a single 2304-dimensional fused embedding vector This avoids the architectural fragility of fine-tuning, allowing us to enhance our existing embedding-based workflows with new modalities more flexibly.All of our data has audio and video; we zero-pad for missing timed text data, which is relatively likely to occur (e.g., in shots without dialogue).The title-level tasks couldn’t be evaluated with the VertexAI MM and Marengo embedding models as the videos exceed the length limit set by the APIs.AcknowledgementsWe would like to thank Matt Thanabalan and Chaitanya Ekanadham for their contributions to this work.MediaFM: The Multimodal AI Foundation for Media Understanding at Netflix was originally published in Netflix TechBlog on Medium, where people are continuing the conversation by highlighting and responding to this story.
Stripe Engineering
1. Supporting additional payment methods for agentic commerce
URL: https://stripe.com/blog/supporting-additional-payment-methods-for-agentic-commerce
Published: 2026-03-03 00:00
Summary:
Stripe is now the first and only provider that supports both agentic network tokens and buy now, pay later tokens in agentic commerce through a single primitive.
2. Can AI agents build real Stripe integrations? We built a benchmark to find out
URL: https://stripe.com/blog/can-ai-agents-build-real-stripe-integrations
Published: 2026-03-02 00:00
Summary:
State-of-the-art LLMs can now solve a majority of scoped coding problems, but it’s an open question whether they can fully autonomously manage software engineering projects We spent months building evaluation environments to benchmark how well AI agents can create real Stripe integrations.
Generated on 2026-03-06 09:35:56